pacman::p_load(readxl, gifski, gapminder,
plotly, gganimate, tidyverse)hands_onExercise3(2)
Programming Animated Statistical Graphics with R
##4.1.1 Basic concepts of animation ##4.2 Getting Started ###4.2.1 Loading the R packages
###4.2.2 Importing the data
# Load libraries
library(readxl)
library(dplyr)
# Correct path
col <- c("Country", "Continent")
globalPop <- read_xls("C:\\RBSantigari-MITB\\ISSS608-VAA\\HandsonExercise\\Hands_onExercise3(2)\\data\\GlobalPopulation.xls",
sheet = "Data") %>%
mutate(across(all_of(col), factor)) %>%
mutate(Year = as.integer(Year))###Unfortunately, mutate_each_() was deprecated in dplyr 0.7.0. and funs() was deprecated in dplyr 0.8.0. In view of this, we will re-write the code by using mutate_at() as shown in the code chunk below.###
col <- c("Country", "Continent")
globalPop <- read_xls("C:\\RBSantigari-MITB\\ISSS608-VAA\\HandsonExercise\\Hands_onExercise3(2)\\data\\GlobalPopulation.xls", sheet = "Data") %>%
mutate_at(col, as.factor) %>%
mutate(Year = as.integer(Year))###Instead of using mutate_at(), across() can be used to derive the same outputs.###
col <- c("Country", "Continent")
globalPop <- read_xls("C:\\RBSantigari-MITB\\ISSS608-VAA\\HandsonExercise\\Hands_onExercise3(2)\\data\\GlobalPopulation.xls", sheet = "Data") %>%
mutate(across(all_of(col), as.factor)) %>%
mutate(Year = as.integer(Year))##4.3 Animated Data Visualisation: gganimate methods ###4.3.1 Building a static population bubble plot
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') 
###4.3.2 Building the animated bubble plot
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') +
transition_time(Year) +
ease_aes('linear') 
##4.4 Animated Data Visualisation: plotly ###4.4.1 Building an animated bubble plot: ggplotly() method
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young')Warning in geom_point(aes(size = Population, frame = Year), alpha = 0.7, :
Ignoring unknown aesthetics: frame
ggplotly(gg)Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
replace is not a multiple of replacement length
#Notice that although show.legend = FALSE argument was used, the legend still appears on the plot. To overcome this problem, theme(legend.position=‘none’) should be used as shown in the plot and code chunk below#
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young') +
theme(legend.position='none')Warning in geom_point(aes(size = Population, frame = Year), alpha = 0.7):
Ignoring unknown aesthetics: frame
ggplotly(gg)Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
replace is not a multiple of replacement length
###4.4.2 Building an animated bubble plot: plot_ly() method
bp <- globalPop %>%
plot_ly(x = ~Old,
y = ~Young,
size = ~Population,
color = ~Continent,
sizes = c(2, 100),
frame = ~Year,
text = ~Country,
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
) %>%
layout(showlegend = FALSE)
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